Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Diffusion models have exhibit exceptional performance in text-to-image generation and
editing. However, existing methods often face challenges when handling complex text …

Abstract Text-to-Image (T2I) and multimodal large language models (MLLMs) have been
adopted in solutions for several computer vision and multimodal learning tasks. However, it …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Diffusion models have demonstrated exceptional efficacy in various generative applications.
While existing models focus on minimizing a weighted sum of denoising score matching …

Despite the success achieved by existing image generation and editing methods, current
models still struggle with complex problems including intricate text prompts, and the …

Conditional diffusion models have exhibited superior performance in high-fidelity text-
guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion …

Despite recent promising performances across a range of vision tasks, vision Transformers
still have an issue of high computational costs. Recently, vision prompt learning has …

Despite recent advances in UNet-based image editing, methods for shape-aware object
editing in high-resolution images are still lacking. Compared to UNet, Diffusion Transformers …

Sign Language Production (SLP) aims to convert text or audio sentences into sign language
videos corresponding to their semantics, which is challenging due to the diversity and …